Optimizing CPU Cache Utilization in Cloud VMs with Accurate Cache Abstraction
Mani Tofigh, Edward Guo, Weiwei Jia, Xiaoning Ding, Jianchen Shan
本文表明,由于对已配置的缓存的可见性和控制有限,基于缓存的优化在云虚拟机(VM)中通常无效。 在公共云中,CPU缓存可以在VM之间分区或共享,但VM不知道缓存配置详细信息。 此外,VM 无法通过页面放置策略影响缓存使用,因为内存到缓存映射是隐藏的。 本文提出了一种新的解决方案CacheX,它使用驱逐集在VM中探索准确和细粒度的缓存抽象,而无需硬件或虚拟机管理程序支持,并使用两种新技术展示了探询信息的效用:LLC争量感知任务调度和虚拟色彩感知页面缓存管理。 我们对 x86 Linux 内核中 CacheX 的实现进行评估,证明它可以有效地提高公有云虚拟机中各种工作负载的缓存利用率。
This paper shows that cache-based optimizations are often ineffective in cloud virtual machines (VMs) due to limited visibility into and control over provisioned caches. In public clouds, CPU caches can be partitioned or shared among VMs, but a VM is unaware of cache provisioning details. Moreover, a VM cannot influence cache usage via page placement policies, as memory-to-cache mappings are hidden. The paper proposes a novel solution, CacheX, which probes accurate and fine-grained cache abstrac...
